Agentic AI Workforce
AI FTEs that work 24/7 for USA healthcare providers
We build and deploy autonomous AI FTEs for USA healthcare providers—including RCM Employee (rcmemployee.com), which we built for XEHR LLC, for revenue cycle management. From workflow automation and RCM automation to custom agents for administrative tasks, our AI workforce handles complex multi-step operations end-to-end.
RCM Employee (rcmemployee.com)
Workflow Automation
RCM Automation
Custom Agent Development
Autonomous Agents
Multi-Step Workflows
Self-Learning
Human-in-the-Loop
HIPAA Compliant
24/7 Operations
Full Observability
Meet your AI workforce
Specialized agents that handle every aspect of healthcare operations with expert-level competence.
Intake Agent
Handles patient registration, demographic verification, and insurance eligibility.
Scheduling Agent
Manages appointments, handles rescheduling, and optimizes provider calendars.
Billing Agent
Processes claims, manages denials, and handles payment posting.
Clinical Agent
Assists with documentation, coding, and clinical decision support.
Beyond simple automation
Autonomous Decision Making
Agents make intelligent decisions based on context, policies, and learned patterns without constant supervision.
Multi-Step Workflows
Complex tasks are broken down and executed across multiple systems with full context awareness.
Natural Language Interface
Communicate with agents using natural language for task assignment and status updates.
Full Observability
Complete visibility into agent actions, decisions, and reasoning with audit trails.
Self-Learning
Agents continuously improve from feedback and outcomes to optimize performance.
Human-in-the-Loop
Configurable escalation paths for decisions requiring human judgment or approval.
Platform Architecture
Built for scale, security, and reliability
US healthcare administrative workflow automation
American healthcare spends an extraordinary share of every dollar on administration rather than care. The spending is not primarily clinical: it is eligibility checks, prior authorisations, claim submissions, denial appeals, credentialing packets and status calls, performed by people moving data between systems that do not talk to each other.
This is the work agentic AI is genuinely suited to. Each task is high-volume, rule-bound but not rigid, and requires reading unstructured documents and acting on them. An AI FTE — an autonomous digital worker that owns a workflow end to end rather than assisting with a single step — is the delivery model that matches the shape of the problem.
Front office and patient access
Eligibility and benefits verification, prior authorisation and pre-certification, scheduling and reminders, intake and registration, referral management, and Good Faith Estimates. This is where the largest share of preventable downstream denials originates.
Mid-cycle and clinical administration
Diagnosis and procedure coding, evaluation and management levelling, clinical documentation integrity queries, charge capture and reconciliation, and quality measure abstraction for programmes such as MIPS and HEDIS.
Back office and revenue cycle
Claim scrubbing and submission, clearinghouse rejection handling, remittance and payment posting, denial management and appeals, accounts receivable follow-up, underpayment detection, patient statements and credit balance resolution.
Credentialing and provider operations
Payer credentialing and re-credentialing, CAQH profile maintenance, provider enrollment and contract management. Slow credentialing directly delays revenue, because an unenrolled provider cannot bill regardless of how much care they deliver.
Compliance and audit
HIPAA audit trail generation, access review, policy attestation tracking and documentation retention. Automation helps here precisely because the requirement is completeness and consistency rather than judgement.
Patient communication
Billing questions, statement explanations, payment plan negotiation and appointment logistics. These consume enormous front-desk capacity while following a narrow set of patterns, which makes them well suited to conversational agents with human escalation.
What makes an agent safe to deploy
An autonomous system operating on protected health information and submitting claims to federal payers needs constraints proportionate to what it can do. The controls are not optional extras; they determine whether the system is deployable at all.
That means a Business Associate Agreement covering every component that touches PHI, encryption in transit and at rest, least-privilege access scoped per agent, and an immutable audit trail recording every action and the reasoning behind it. It also means explicit escalation thresholds, so that decisions above a defined dollar value or confidence level route to a human by design, and a rollback path for any action the system takes.
A shipped AI FTE, and custom agent fleets
RCM Employee, which we built for XEHR LLC, is the clearest demonstration of the model: an autonomous digital worker that owns eligibility, coding, claims, denials and payment posting for US healthcare providers, around the clock, with full audit trails. It is in production, not in a pilot deck.
Beyond it we design custom agent fleets for organisations whose back-office workflows no single SaaS can model — planner, executor and reviewer roles bound together with policy enforcement, escalation paths, long-running task memory, and full observability across traces, costs and decisions, deployed on GCP, AWS or on-premise. One such build is already shipped and complete: a custom AI agent that automates ActiveCampaign for patient lead capture, routing and follow-up, running across a client's marketing stack.
Questions we get asked most
What is an AI FTE?
An AI FTE is an autonomous digital worker that owns an administrative workflow end to end rather than assisting with an individual task. Where a copilot suggests and waits, an AI FTE is assigned a queue — eligibility verification or denial follow-up, for example — and works it continuously, escalating only the cases that need human judgement.
How is this different from robotic process automation?
RPA replays a fixed sequence of interface actions and breaks when the interface or the process changes. Agentic systems are given a goal and a set of tools and choose their own path, so an unfamiliar form or an unexpected denial reason is something to interpret rather than a failure condition.
Is agentic AI HIPAA compliant?
Compliance is a property of the deployment, not of the technology. It requires a Business Associate Agreement with every vendor processing PHI, encryption in transit and at rest, role-based least-privilege access, audit logging of all access, and a documented incident response process. An AI system inherits exactly the same obligations as any other system handling PHI.
Which workflow should we automate first?
The best first candidate is high-volume, low-variation and low clinical risk, which usually means eligibility verification, claim status checks or payment posting. These produce measurable results quickly and build the operational confidence needed before automating judgement-heavy work such as appeals or coding.
What happens when an agent is uncertain?
It escalates. Confidence thresholds, dollar-value limits and defined exception categories route work to a human queue rather than allowing the agent to guess. A system without a well-designed escalation path is not safe to run on financial or clinical data, regardless of how accurate it is on average.
How long does deployment take?
Timelines are driven by integration and access rather than by the agent itself. Where a practice can provide clearinghouse access and an EHR interface, a first workflow can typically run in a supervised mode quickly, with the supervised phase existing to calibrate escalation thresholds against real cases before autonomy is widened.
Let's build the future together
Ready to transform your business with AI and robotics? Reach out to us.